Grey Wolf Optimization Algorithm with Improved Convergence Factor and Position Update Strategy

Abstract

For a high search accuracy and overcoming the problem of tangling the local optimum of grey wolf optimization (GWO) algorithm, a nonlinear convergence factor combining tangent and logarithmic functions is proposed to dynamically adjust the global search ability of the algorithm. An adaptive position updating strategy is also introduced to accelerate the convergence speed of the algorithm in the process of convergence. The experimental results on benchmark functions show that the improved algorithms outperform the standard grey wolf algorithm in convergence speed, stability and optimization accuracy.